The present subject matter relates generally to manufacturing and, more particularly, to tuning a process controller based on a dynamic sampling rate.
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a group of wafers, sometimes referred to as a “lot,” using a variety of processing tools, including photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, implantation tools, etc. The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various processing tools. The manufacturing tools communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process, various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability. Metrology data collected before, during (i.e., in-situ), or after the processing of a wafer or lot of wafers may be used to generate feedback and/or feedforward information for use in determining a control action for the previous process tool (i.e., feedback), the subsequent process tool (i.e., feedforward), or both.
Run-to-run (RtR) control is commonly used in semiconductor manufacturing to compensate for process disturbances and the therefore regulate processes at a predetermined target. Typically, the controller adjusts an operating recipe for a controlled tool using feedback or feedforward metrology information. Control actions are typically generated using a control model that tracks one or more process state variables associate with the fabrication. For example, a controller may adjust a photolithography recipe parameter to control a critical dimension (CD) of the manufactured devices. Likewise, a controller may control an etch tool to affect a trench depth or a spacer width characteristic.
RtR controllers use historical data to first estimate the state of the process and then calculate the process inputs based on this state estimation. The state is usually assumed to be an integrated moving average (IMA) process, and it is well known that the exponentially weighted moving average (EWMA) filter typically provides the optimal forecast of an IMA process. An RtR controller using the EWMA filter for state estimation is commonly referred to as an EWMA controller.
An EWMA filter uses a weighted average in that the average is more heavily affected by the more recent state values. The EWMA filter is tuned using a tuning factor that regulates how quickly historical data is discounted. If the tuning parameter is close to zero, then the data is discounted very slowly and nearly all available historical data is considered nearly identically when calculating the state of the system. In contrast, when the tuning parameter is close to one, then only the most recent measurements have significant contributions to the state estimate.
Metrology equipment is expensive; hence, there is not sufficient metrology capacity to measure every wafer or lot in the fabrication flow. In addition, measuring wafers is time consuming, which increases cycle time and decreases the throughput of the manufacturing process. Typically, RtR controllers are implemented in conjunction with a sampling application. The tuning parameters for the RtR controller are typically determined empirically prior to implementation. The specifying of the tuning parameters involves various assumptions, including the expected sampling rate of the process. However, this assumption regarding sampling rate can be faulty, resulting in a poorly tuned controller. Moreover, the metrology capacity of the facility may change due to different sampling priorities, equipment maintenance, etc. These changes in sampling capacity affect the achievable sampling rate. Such changes can also result in the controller being poorly tuned, and, as a result, suboptimal.
This section of this document is intended to introduce various aspects of art that may be related to various aspects of the present subject matter described and/or claimed below. This section provides background information to facilitate a better understanding of the various aspects of the subject matter. It should be understood that the statements in this section of this document are to be read in this light, and not as admissions of prior art. The present subject matter is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.